• The GRINS foundation aims at implementating a Data platform for the transfer of knowledge and statistical analysis (AMELIA)

• Prime matter of the platform: the data lake

  • Broad repository hosting several categories of administrative data from different sources
  • Available to either private, corporate or academiic users
  • Data organised at the territorial level of municipalities (LAU/NUTS-4)

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• The present R package is intended as a one of the several contributions to the data lake

• This package covers the dimension of public education, with special regards to the territorial structure of the education system.

• Main utility: analysing territorial disparities in education quality and school infrastructure endowment

• Accessibility: All data must be publicly accessible and easy to handle for the generic user
  • Input data are open and come from publicly accessible web pages
• Updating: All information is retrieved in real time in order to be up-to date
  • Inputs are scraped from the web rather than stored in a built-in repository
• Portability: All objects should be easy to export and process with different softwares:
  • We work in the framework, and all outputs are structured as tibbles

• Get_: input data scraping. Information is not altered and the user receives a data set as close as possible as the provider releases it

• Util_: utilities; mainly data modification and editing

• Group_: data aggregation at the relevant territorial level

  • NUTS-3/Province
  • LAU/Municipality

• Map_: displaying

  • Static maps (vector format): easy to export
  • Interactive maps: preserve information on different variables

• Data from the Ministry of Education
  • Includes:
    • National Schools Registry
    • School Buildings database
    • Students and teachers counts
  • Mainly available at the school level (except for the count of teachers)
• Ultra - Broadband implementation
  • Available at the school level
• Invalsi census survey
  • Available at the NUTS-3 / LAU level

• Schools ID - mechanographical codes
  • Most complete list: National Schools Registry
  • Identifies both school order and address (of high schools)
• School buildings ID - typically numeric codes
  • Only included in the School buildings DB

• Main source of information regarding the school infrastructure

• Mostly includes categorical variables, regarding several aspects such as:

  • Environmental context
  • Reachability by public or private transport
  • Building period
  • Surfaces and volumes

• As an example, in the next slides we display middle schools area surface (on logarithmic scale to ease the comparison) for the three regions of Apulia, Basilicata and Calabria.

Functions: - Get_DB_MIUR() Scrape the raw data and return the school-buildings level database - Util_DB_MIUR_num() Convert raw data to numeric and edit if required - Group_DB_MIUR() Harmonise at the territorial level - Map_SchoolBuildings() Render

Input_DB23_MIUR <- Get_DB_MIUR(Year = 2023, 
                               input_Registry = Registry23) 
## ## 2022/23 is the latest year available
#à ## then, remember adding message = FALSE

DB23_MIUR_mun <-  Group_DB_MIUR(Input_DB23_MIUR, InnerAreas = FALSE
                                )$Municipality_data %>% 
  dplyr::mutate(log_Surface = log(.data$School_area_surface))

head(DB23_MIUR_mun)

DB23_MIUR_mun %>% 
  Map_School_Buildings(input_shp = Mun22_shp, field = "log_Surface", 
                       level = "LAU", order = "Middle",
                       region_code = c(16, 17, 18), verbose = FALSE)

• National Ultra - Broadband plan (2020): regards about \(35.000\) schools over the national territory (the vast majority)
• Plan expected to be fulfilled by 2023 EoY; however, works are still in progress for a number of schools
• By definition, Ultra - BroadBand connection has a minimum guaranteed speed of 100 megabits/second until the peering, with a maximum of 1 Gigabit/second

BB24 <- Get_BroadBand(Date = as.Date("2024-01-01"), verbose = FALSE) %>% 
  dplyr::filter(.data$Order == "High") %>% 
  dplyr::group_by(.data$Region_description) %>% 
  dplyr::summarise(Status = mean(.data$BB_Activation_status)) 

• Aggregate measure of students skills, expressed either as the territorial average of:
  • Percentage of sufficient tests (primary schools only)
  • Ability of \(i\)-th student (\(A_i\)) to answer correctly the question \(Q_j\) of difficulty \(D_j\), based on the model \[Prob \lbrace Q_{ij} = 1 \rbrace = \frac{e^{A_i - D_j}}{1 + e^{A_i - D_j}}\]
• Spatially homogeneous indicator
• Three variables: M_: mean; S_: standard deviation; C_: coverage

Map_Invalsi(input_shp = Prov22_shp, grade = 13, subj = "MAT", 
            level = "NUTS-3", Year = 2023, verbose = F, main ="Math score")